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Infrared and visible images registration with adaptable local-global feature integration for rail inspection

Lookup NU author(s): Chaoqing Tang, Professor Gui Yun Tian, Dr Jianbo Wu, Kongjing Li

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Abstract

© 2017 Active thermography provides infrared images that contain sub-surface defect information, while visible images only reveal surface information. Mapping infrared information to visible images offers more comprehensive visualization for decision-making in rail inspection. However, the common information for registration is limited due to different modalities in both local and global level. For example, rail track which has low temperature contrast reveals rich details in visible images, but turns blurry in the infrared counterparts. This paper proposes a registration algorithm called Edge-Guided Speeded-Up-Robust-Features (EG-SURF) to address this issue. Rather than sequentially integrating local and global information in matching stage which suffered from buckets effect, this algorithm adaptively integrates local and global information into a descriptor to gather more common information before matching. This adaptability consists of two facets, an adaptable weighting factor between local and global information, and an adaptable main direction accuracy. The local information is extracted using SURF while the global information is represented by shape context from edges. Meanwhile, in shape context generation process, edges are weighted according to local scale and decomposed into bins using a vector decomposition manner to provide more accurate descriptor. The proposed algorithm is qualitatively and quantitatively validated using eddy current pulsed thermography scene in the experiments. In comparison with other algorithms, better performance has been achieved.


Publication metadata

Author(s): Tang C, Tian GY, Chen X, Wu J, Li K, Meng H

Publication type: Article

Publication status: Published

Journal: Infrared Physics and Technology

Year: 2017

Volume: 87

Pages: 31-39

Print publication date: 01/12/2017

Online publication date: 21/09/2017

Acceptance date: 16/09/2017

ISSN (print): 1350-4495

Publisher: Elsevier B.V.

URL: https://doi.org/10.1016/j.infrared.2017.09.013

DOI: 10.1016/j.infrared.2017.09.013


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